Wildlife in climate refugia: Mammalian diversity, occupancy, and tiger distribution in the Western Himalayas, Nepal

Abstract Anthropogenic land‐use change continues to be predicated as a major driver of terrestrial biodiversity loss for the rest of this century. It has been determined that the effect of climate change on wildlife population will accelerate the rate and process of decline of global vertebrate populations. We investigated wildlife composition, occupancy, and activity pattern along the larger climate resilient forests that serve as microrefugia for a wide range of species under the escalating climate change. We used camera trap survey covering 250 km2 of climate microrefugia in Dadeldhura hills in far western region of Nepal. We used 62 trapping locations accumulating 1800 trap nights taking 98,916 photographs in 62 days‐survey period during the summer season of 2020. We photographed 23 mammalian species with estimated species richness of 30 species (95% CI: 25–34) based on multi‐species occupancy model. We estimated overall species occupancy ψ(SE(ψ)) to be 0.87 (0.09) in climatic microrefugia. While human activity predominated throughout the day, the majority of animals was found to exhibit nocturnal temporal patterns. Tiger and hyaena, two of the top predators, were newly discovered in the western Himalayan range of Nepal, with their discovery at the 34 highest elevations of 2511 meters and 2000m, respectively. In Nepal, high‐altitude tiger range is characterized by tiger distribution above a 2000 m cutoff representing habitats in the physiographic zone of high mountains and above. Our findings establish a baseline and show that the climatic microrefugia that have been identified have high levels of species richness and occupancy, which characterize the Dadeldhura hill forest ranges as biologically varied and ecologically significant habitat. These areas identified as climatic microrefugia habitats should be the focus of conservation efforts, particularly efforts to reduce human disturbance and adapt to climate change.

cupancy, and activity pattern along the larger climate resilient forests that serve as microrefugia for a wide range of species under the escalating climate change. We used camera trap survey covering 250 km 2 of climate microrefugia in Dadeldhura hills in far western region of Nepal. We used 62 trapping locations accumulating 1800 trap nights taking 98,916 photographs in 62 days-survey period during the summer season of 2020. We photographed 23 mammalian species with estimated species richness of 30 species (95% CI: 25-34) based on multi-species occupancy model. We estimated overall species occupancy ψ(SE(ψ)) to be 0.87 (0.09) in climatic microrefugia. While human activity predominated throughout the day, the majority of animals was found to exhibit nocturnal temporal patterns. Tiger and hyaena, two of the top predators, were newly discovered in the western Himalayan range of Nepal, with their discovery at the 34 highest elevations of 2511 meters and 2000m, respectively. In Nepal, high-altitude tiger range is characterized by tiger distribution above a 2000 m cutoff representing habitats in the physiographic zone of high mountains and above. Our findings establish a baseline and show that the climatic microrefugia that have been identified have high levels of species richness and occupancy, which characterize the Dadeldhura hill forest ranges as biologically varied and ecologically significant habitat. These areas identified as climatic microrefugia habitats should be the focus of conservation efforts, particularly efforts to reduce human disturbance and adapt to climate change.

K E Y W O R D S
activity pattern, microrefugia, multi species occupancy model, occupancy, tiger

| INTRODUC TI ON
Anthropogenic-driven land-use change has caused habitat loss, which has led to the highest rate of population reduction and is expected to continue as a key factor in the loss of terrestrial biodiversity for the rest of this century (Sala et al., 2000). Global vertebrate species population has decreased by an estimated 68% in the last 50 years (WWF, 2022). Climate change impact on species is not yet adequately documented by International Union for Conservation of Nature's (IUCN) Red List of Threatened Species (Akcakaya et al., 2006). However, climate change issue has been directly implicated in the deteriorating status of several vertebrates and may intensify the rate and process of ongoing drivers of decline to hasten extinction (Hoffmann et al., 2010;Laurance & Useche, 2009). It is estimated that 47% of threatened land mammals on the IUCN's Red List of Threatened Species have been negatively affected by climate change (Pacifici et al., 2017). Failing to address threats associated with climate change may further aggravate overall pressure to the biodiversity (Harfoot et al., 2021).
Understanding how drivers of change are interacting and impacting populations, and how this varies spatially, is critical if we are to identify populations at risk, predict species' responses to future environmental changes and produce suitable conservation strategies (Williams et al., 2022). Global Climate Risk Index 2021 ranks Nepal as the tenth most vulnerable country globally between 2010 and 2019 (Eckstein & Schäfer, 2021) based on extreme weather events and indicating a high level of exposure and vulnerability.  identified larger climate resilient forests as macrorefugia (including microrefugia within it) that needs to be conserved to limit the impacts of rising global temperatures in the 21st century (Ashcroft, 2010;Rull, 2009) for providing refuge to a wide range of species (small to large). Refugia are habitats that components of biodiversity retreat to, persist in and can potentially expand under changing environmental conditions (Keppel et al., 2012) triggered by climate change. Often, these habitats are also at risk of local species extinction from ongoing threats (Green et al., 2019) and future climate vulnerabilities (Bellard et al., 2012;Leclerc et al., 2020). Thus, understanding the pattern and drivers of species distribution and abundance is important for species conservation planning perspective under changing environmental conditions. Mammalian diversity across the protected areas system is among the highest in South Asia which represents approximately 9.4% of the world's mammalian diversity (Srinivasulu, 2019). Existing large forest connectivity joining the impeding protected areas provides space for species dispersal, colonization, and recovery along the restored habitat (Green et al., 2019). Less empirical information is available on wildlife composition, population, and its distribution from area outside the protected areas which are a mix of forests, agriculture lands, and settlements in Nepal. The Churia, also called the Siwalik in India, and Mahabharat ranges are the youngest and highest mountain ranges, beside snowcapped Himalayas in Nepal. Kelly (2016, 2017) carried out first detail systematic survey of wildlife in the Nepal's forgotten tiger land-Churia-within the protected areas. Ecological relevance of Churia habitat for tiger conservation (Thapa & Kelly, 2017) contributed to nationwide survey of Churia physiographic zone for faunal assessment . It is often argued that climate change and human disturbances are anticipated to alter both wildlife distributions and their movement patterns, increasing the risk of defaunation and habitat destruction for many endangered species and ecosystem (Gaynor et al., 2018;Li et al., 2019). Wild animals co-occur with humans, animals may minimize risk by separating themselves in time rather than in space (Kronfeld-Schor & Dayan, 2003). Temporal partitioning is a common, even basic phenomenon shaping spatiotemporal patterns of predation and competition (Gaynor et al., 2018). No research so far has been conducted on composition of wildlife communities, temporal pattern of wildlife, and potential species distributionparticularly mammals-in the targeted habitat identified as macro climate-refugia  which are distributed along these physiographic zones-lowland areas, Churia, middle mountain, and high mountain. Camera trapping, as employed here, provides an added opportunity to gain insight into species diversity (Rahman et al., 2021) and activity of Asian forest fauna that are difficult to see or are rare (Lynam et al., 2013).
In South Asia, tigers (Panthera tigris tigris) and leopards (Panthera pardus fusca) are found to occupy a wide range of habitats including alluvial floodplain grasslands, seasonally dry subtropical deciduous forests in the lowlands (Odden et al., 2010), the Bhabar (Thapa et al., 2014), the Churia (Thapa & Kelly, 2017), stretching beyond the subtropical into temperate areas up to alpine regions in the Himalayas for leopards (Wang & Macdonald, 2009) and mangroves deltas for tigers in the Sundarbans (Loucks et al., 2010). Majority of identified climatic-refugia sites are either contiguous within species range (insitu refugia) and/or at distant to existing tiger habitat (exsitu refugia) in Nepal. Hence, with climate change impacts and tiger population on the rise in Nepal, tigers are known to be dispersing into newer habitats (Wikramanayake et al., 2004). Tigers require a functional ecosystem and climate resilient landscapes with undisturbed large tracts of habitat with enough prey for maintaining viable population for their long-term survival (Wikramanayake et al., 2004).
Understanding their presence and distribution along the habitat in high elevational range beyond its known range is crucial. Our objectives were to (1) quantify mammalian occupancy and richness in the climatic microrefugia site, (2) determine the temporal activity pattern of recorded mammalian species, and (3) assess the tiger distribution along the habitat of the high elevation gradient in Nepal. We

T A X O N O M Y C L A S S I F I C A T I O N
Biodiversity ecology, Community ecology, Conservation ecology, Ecosystem ecology used Bayesian analytical platform-multi-species occupancy model (MSOM)-an extension to the Single Species Occupancy Model, to assess presence, community composition, and species richness of mammals in climatic microrefugia site in Western Nepal (Iknayan et al., 2014;Kéry & Royle, 2020). This is the first attempt to quantify estimates of mammalian richness and occupancy, as well as the intrinsic temporal pattern of wildlife in the western landscape, and it provides important insight into what actions are required to conserve wildlife in an area outside the protected areas that are likely to be more resilient to climate change in the future.

| Study area
Five broad physiographic ranges are found in Nepal-lowland (Terai, to other 76 districts in the country. Over 58% (~145 km 2 ) of the forest area in study area has been identified as climatic microrefugia . Pinus spps, hill sal (Shorea robusta) forest, and Quercus-Rhododendron spps are major tree species found in the region. Microclimatic variables (temperature, precipitation, and humidity) vary along the elevational range (min: 418 m-max: 2547 m) suggesting variation in habitat niche. Average temperature ranges between minimum of 4°C in the month of December in winter season and maximum of 27.5°C in the month of July in monsoon season. Precipitation is estimated to be as high as 1115 mm during the monsoon to as low as 53 mm in the post monsoon season. Snowfall has been recorded at an elevation higher than 2000 m asl (above sea level) encompassing 37% of our study area.

| Camera trap survey
We conducted camera trap survey for 62 days in the post winter season from March to May 2020. We sampled 250 km 2 of forested habitat identified as climate refugia located in Mahabharat range ( Figure 1).
We divided survey areas into two blocks each measuring an average 125 km 2 as these sites were mostly inaccessible and to complete the survey in single block design due to logistical limitations. Each of the blocks were further divided into 2 × 2 km grid cells and suitable sites identified within each cell ( Figure 1) for setting up camera stations.
We set pairs of Cuddeback™ cameras at 62 locations and sites chosen to maximize detection and ensure ease in accessibility. Each camera trap station at each block was active for ~31 days. We followed the fourth design protocol (Karanth & Nichols, 2002) with a rotation of camera traps between the blocks sequentially covering the area of interest. We placed camera traps along the ridgelines, cliff bases, stream gorges, firelines, and trails commonly used by wild animals and people.
Cameras were tied either to trees or fixed poles at the height of ~40 cm from the ground. The survey was designed to maximize capture probabilities of mammalian wildlife of varying body sizes and shoulder heights known to use the habitat traversing along similar travel routes.
Cameras were placed ~3 m away on either side of the movement trail to ensure full-body capture of mammals in the area. The inter-trap distance between two consecutive locations was ~1.8 km (SD 0.3), with elevation ranges between 418-2457 m asl ( Figure 2). F I G U R E 1 Study area showing camera trap grid (2 km by 2 km) and camera trap station (represented by black dot) including climatic microrefugia. Shuklaphanta National Park and Khaptad National Park are two nearest protected areas located in the lowland and mountain areas.

| Species identification and capture events
After careful retrieval of all images from the camera traps, we manually stored them in memory card in relevant folders. We used the Baral and Shah (2008) manual for species identification in camera trap images. We sorted the images, considering the photographs as independent events if they were 30 min or more apart, unless we could tell they were distinctly different individuals, as is commonly done in camera trap studies (Di Bitetti et al., 2006;Silver et al., 2004).
We found it difficult to individually identify each wildlife captured, and thus, used capture events (number of independent photographs) per unit effort (100 trap nights) as a measure of the trapping rate or relative activity of wildlife (Kelly, 2008;Rovero & Marshall, 2009).
There is a constraint associated with index-based count (used here as the trapping rate or relative activity of wildlife) (Gopalaswamy et al., 2015;Tobler et al., 2015). Although index-based count do not imply true abundance of the wildlife species found in the study, use of such indices has been validated in camera trap studies (Carbone et al., 2001;Rahman et al., 2021;Thapa & Kelly, 2017). We segregated identified wildlife as per the relevant taxa and IUCN's Red list of threatened species categories (IUCN, 2012).

| Species richness
We used MSOM to estimate species-specific occurrence probabilities (Dorazio & Royle, 2005;Zipkin et al., 2010) while correcting for incomplete detection (MacKenzie et al., 2002). MSOM is known to leverage information from across the community, and rare or poorly detected species can be analyzed individually by "borrowing" data from the community (Iknayan et al., 2014). This approach seems feasible for this study as very limited information on species diversity was available from this area. We followed Bayesian approach to MSOM as used by Rahman et al. (2021) in Bangladesh for estimating species richness. Information about prior and basic code used here is available as Appendix S1. Undetected species for which no data were represented by including all-zero encounter histories in a process known as zero augmentation (Kéry & Royle, 2009) and helped to assess the occupied range of mammal species and species richness (Broms et al., 2016;Kéry & Royle, 2020). We used data augmentation value (for undetected species) of 10 for estimating species richness and evaluated the results to explain the best possible species richness estimates for the study areas.
We estimated posterior distributions of parameters using Markov Chain Monte Carlo (MCMC) implemented in JAGS (version 3.4.0) which we called using R2Jags (Plummer, 2011) in R (R Core Development Team 3.2.2). We generated seven chains of 1,000,000 iterations after a burn-in of 5000 iterations and thinned by 100. We assessed convergence using the Gelman-Rubin statistic (R) where values <1.1 indicated convergence (Gelman & Rubin, 1992).
Here, we estimated three parameters: (1) ψ t , probability of occupancy, defined as the probability of species occurring at camera trap station, (2) p s , probability of detection, the probability that a species is detected given that it is present, and (3) omega, overall probability of occupancy across species presented in the study areas. We presented predictive maps using mean site wise species richness estimates based on standard null model (without covariates). Due to small number of sites and low capture rate of many of the species along the location, there was issue with non-convergence, and so, no covariates were used for assessing the factor affecting the occupancy.

| Activity pattern
We looked at daily rhythms of wildlife activity looking at the time of day, which is circular pattern, and applied circular statistics using the program ORIANA (Kovach Computing Services;Kovach, 2012).
Frequency of camera trap images of a species in time reflect temporal activity of the species (Rowcliffe & Rowcliffe, 2016). We described temporal activity patterns for each camera trap recorded species by fitting a von Mises probability density distribution (Fisher, 1995) as done with tiger and its copredators in India (Karanth et al., 2017). We used the time stamp data showing the time of encounter obtained from camera trap images to compute the temporal activity pattern of species captured in the study area. All encounters were collapsed F I G U R E 2 Camera trap stations (n = 62) deployment along the elevation range in the study area. into a single 24-h period. We segregated and assigned the temporal activity for a species to be diurnal, crepuscular (i.e., active primarily at twilight), nocturnal or cathemeral (i.e., irregularly active at any time of day or night). We also compared temporal activity of the recorded species from the study area with that from protected area in low-lying terai (Shuklaphanta National Park) and high mountains (area along the similar range in Asia).

| Tiger distribution and connectivity in climate refuge
We carefully searched for tiger detection in camera trap locations in the present survey to ascertain its range along the northern frontier of western Himalayan range.   were also recorded (Table 1). Himalayan black bear (Ursus thibetanus) has also been recorded but outside the sampling period in the study area. our camera-station specific estimates of mammalian richness ranged from 3 (95% CI: 1-5) to 10 (95% CI: 9-12) with a mean estimated richness per camera trap station of 6 ( Figure 3).

| Temporal activity of the wildlife and human across the refugia
High human presence has been recorded along the forested area with probability of occupancy estimated at 0.92 (SD 0.03) with their activity identified during day time (Figure 4). Temporal interactions between wildlife and human were inversely correlated with wildlife depicting an overall crepuscular and nocturnal activity (active at dawn and dusk, Figure 4). All the carnivores species recorded in the study areas were found to be active during the night except golden jackal, which was active most of the time showing cathemeral behavior ( Figure 4). Among the herbivores, highly abundant barking deer was active day and night including sambar-largest of the deer species recorded in the study area. The Tarai gray langur and Nepal gray langur-showed diurnal temporal pattern within the study areas. We found variations in temporal activity patterns among the recorded species in the study area in comparison with those in other protected areas. More than half (~50%-71%) of the recorded species were either strictly nocturnal or cathemeral (Table 2).

| Detection of tiger in climatic microrefugia
From the stacks of images representing 24 terrestrial species, we recorded the photographic evidence of tiger (probably male) with two unique detections at 16:00 p.m. at elevation of 2511 m asl on the western aspect of Mahabharat range in western Nepal ( Figure 5). Within the study area, tiger occupancy is estimated at 0.03 (0.02). Comparatively, 72% of tiger captures were recorded in terai along the elevation range <300 m, 26% in Churia between 300-700 m and rest 2% above 700 m ( Figure 6). Among the recorded tiger locations in Nepal, approximately 14% falls within habitat identified as climate microrefugia.

| DISCUSS ION
In Nepal, the Dadeldhura hill forest ranges have been identified as a climatic microrefugia and as biologically diverse and ecologically TA B L E 1 Camera trap photo evidence with its estimated occupancy (Ψ), trapping rate, camera trapped in study area, recorded elevation range, naïve occupancy, and corresponding trapping rate for each photo captured species.

| Species richness in climate microrefugia
Our study is the first to focus on mammalian diversity outside the protected areas system in Nepal and in habitat identified as climatic microrefugia   (Bhandari et al., 2020) and their presence has been reported upto 1750 m (Majupuria & Majupuria, 2006).
The present study photo captured hyaena's presence at an elevation of 2030 m ( Figure 5)-the first record of this species at this altitude.
Species such as spotted deer, Himalayan black bear, dhole, Himalayan serow, Assamese macaque have a possible range within study areas (Baral & Shah, 2008) and could be pseudo absent during the effective sampling period for this study. Dadeldhura hill forests provide suitable habitats with a potential distribution of arboreal species such as red panda  in Chitwan National Park (Rich et al., 2014) (Soulé & Noss, 1998), ensuring comparable species richness to nearby protected areas. Since late 70s, most of forest stands along the periphery of settlements in the districts have been handed over to the community as community forest where by forest resources are used and managed by the F I G U R E 3 Site level species richness estimates variablity along the identified climatic microrefugia in the western part of Nepal.

F I G U R E 4
Proportion of encounters of the human and wildlife during daytime (06:00-17:59 h; hashed wedges above half circle) and nighttime (18:00-05:59 h; hashed wedges below half circle) in climatic microrefugia. Rose diagrams were generated using ORIANA (Kovach, 2012). Each plot is divided into 24 h, with percentage of detections in each hour on the response axis.
communities. The community forestry (CF) program is well institutionalized in Nepal with handing over the management of the forest to the communities living nearby (Acharya, 2002). A well- Nandhaur Wildlife Sanctuary, Champawat, and Boom Forest range TA B L E 2 Comparison of temporal activity patterns of recorded species with the nearest protected areas in lowland areas (Shuklaphanta National Park or other lowland protected areas) and available published data from mountain protected areas.

Temporal activity in lowland area in ShNP
Temporal activity from mountain protected areas References Site has been identified as climatic microrefugia .
across the border in India. The applicability of MSOM utilizing the camera trap data provided very useful and important metrics in a single framework for faunal assessment.

| Temporal activity of wildlife and human
Majority of the wildlife species, and especially carnivores, were found to be active during the nighttime. Leopards and leopard cats were found to be active during the nighttime, as was found in Langtang National Park (Can et al., 2020) with similar landscape heterogeneity. Barking deer (both diurnal and nocturnal) and wild boar (mostly nocturnal) were both detected through the elevational gradient and represents the principal prey for mid-sized carnivores such as common leopard and hyaena. From our data and other studies in lowland Nepal (Poudyal et al., 2019) and Thailand (Lynam et al., 2013), leopard cats are strongly nocturnal. Himalayan goral-a goat species-largely confined in Churia (Thapa & Kelly, 2017) and high mountains upto 3200 m asl  were largely active during day time, while being less active at night and with activity peak higher in the morning time in our study area in concordance with work from Bhattacharya et al. (2012) and could be potential prey for the common leopard. Yet, Himalayan goral's preference for steeper slopes habitat (Bhattacharya et al., 2012) and thereby energy requirement for predation requires further investigation for their contribution in the carnivore diets. Of the four species of primates found in Nepal (Chalise, 2004) supports this global notion as almost 50%-71% of the species were found to be either strictly nocturnal or cathemeral in the study area.

| Detection of tiger in high altitude habitat
In the Indian-subcontinent, tigers (Panthera tigris tigris) are known to be generalists and occur in a multitude of habitat types featuring a range from saline mangrove forests in India (Roy et al., 2016) to floodplains in Nepal  and going up to 4400 m asl in alpine forest habitat in Bhutan (Tempa et al., 2019). Tiger recorded at an elevation of 3100 m asl in the eastern Himalayan range remains the highest altitudinal record in Nepal (Bista et al., 2021 to larger climatic macrorefugia (~47,400 km 2 ) sites identified in these physiographic zones. More camera trap survey with protocol as defined here are anticipated in high-altitude habitats-focusing on high mountains-also known as the Mahabharat range. Nepal has been successful in achieving doubling of wild tigers in its core areas; additional efforts to identify habitat suitability outside protected areas regime including in Mahabharat range forest areas by intervening in forest and wildlife management seems pertinent in days to come.

ACK N OWLED G M ENTS
We are thankful to the Department of Forests and Soil Conservation and Division Forest office, Dadeldhura District for granting permission to conduct the study. We would like to thank all the field personnel, citizen scientists, and field rangers who helped conduct field surveys. Special mention of field colleagues for help in the coordinating field work through WWF Nepal supported Terai Arc Landscape Project. We would like to thank Hasan A. Rahman for his help in providing the MSOM code for running the analysis and help in interpreting the results. We would like to acknowledge WWF Finland for providing the financial support for conducting the task.

CO N FLI C T O F I NTE R E S T
None.

DATA AVA I L A B I L I T Y S TAT E M E N T
Data related to this article are deposited in Dryad and are available via the following link https://doi.org/10.5061/dryad.9kd51 c5m1.